### Curriculum vitae

A condensed curriculum vitae (as of Jan 24 2019) can be found here, or see my Google Scholar profile.

### Short bio

Marie E. Rognes received her Master (2005) and Ph.D. (2009) degrees in applied mathematics at the Centre for Mathematics for Applications, Faculty of Mathematics and Natural Science, University of Oslo. During 2007, she visited the Institute for Mathematics and its Applications (IMA) at the University of Minnesota Twin Cities, US. She then joined Simula Research Laboratory as a post-doctoral fellow in 2009, before being employed as a Senior Research Scientist in 2012, and as a Chief Research Scientist in 2016.

She led the Biomedical Computing Department at Simula from 2012-2016, the Robust Solvers project at the Center for Biomedical Computing (CoE) from 2012-2017. and the AUQ-PDE project from 2015-2017. In 2015-2016, Rognes was an Adjunct Associate Professor in Solid Mechanics at the Department of Mathematics at the University of Oslo.

In 2015, Rognes won the 2015 Wilkinson Prize for Numerical Software, was elected a Founding Member of the Young Academy of Norway, and received the 2015 Simula Research Award. She was also awarded a FRIPRO Young Research Talents Grant from the Research Council of Norway for the project “Waterscape: The numerical waterscape of the brain”. In 2016, Rognes was awarded an ERC Starting Grant in Mathematics for the project “Waterscales: Mathematical and computational foundations for modelling cerebral fluid flow”. In May 2017, Rognes gave a talk at TEDxOslo titled “Mathematics that cures us” on the topic of mathematical modelling in medicine. In 2018, Rognes won the 2018 Royal Norwegian Society of Sciences and Letters Prize for Young Researchers within the Natural Sciences.

Rognes’ research interests revolve around numerical methods for partial differential equations, in particular the analysis and implementation of structure- and property-preserving discretizations such as mixed finite element methods. She has been a core team member of the FEniCS Project since 2007 and is a co-founder of the dolfin-adjoint project, focusing in particular on automated code generation for sophisticated finite element spaces, automated goal-oriented adaptivity and error control, and automated derivation of adjoints of time-dependent finite element models. Rognes has successfully used these methods and techniques in a range of applications with particular emphasis on modelling and simulation of biological tissue. In the next years, she aims to study and use numerical methods to understand fluid flow and transport within the central nervous system at a range of spatial and temporal scales.

### Publications

For a relatively recent list of publications (articles, book chapters, presentations), see rognes-publications-2018-04-23.

#### Articles in international journals

[28] X. Lai et al, Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data, Cancer Research (accepted), 2019.

[27] V. Vinje, G. Ringstad, M. E. Rognes, P. K. Eide and K.-A. Mardal. Respiratory influence on aqueductal flow in idiopathic normal pressure hydrocephalus. Submitted to journal for publication, 2019.

[26] P. E. Farrell, J. E. Hake, S. W. Funke and M. E. Rognes. Automated adjoints of coupled PDE-ODE systems. Accepted for publication in SIAM Journal on Scientific Computing, 2019.

[25] J. J. Lee, E. Piersanti, K.-A. Mardal and M. E. Rognes. A mixed finite element method for nearly incompressible multiple-network poroelasticity. SIAM Journal on Scientific Computing, 2019.

[24] R. Rodriguez-Cantano, J. Sundnes and M. E. Rognes. Uncertainty in cardiac myofiber orientation and stiffnesses dominate the variability of left ventricle deformation response, International Journal for Numerical Methods in Biomedical Engineering, 2019.

[23] V. Vinje, J. Brucker, M. E. Rognes, K. A. Mardal, V. Haughton. Fluid dynamics in syringomyelia cavities: Effects of heart rate, CSF velocity, CSF velocity waveform and craniovertebral decompression, The Neuroradiology Journal, 2018.

[22] M. Croci, M. B. Giles, M. E. Rognes and P. E. Farrell. Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes. SIAM/ASA Journal Uncertainty Quantification, 2018.

[21] G. Balaban, H. Finsberg, S. Funke, T. F. Håland, E. Hopp, J. Sundnes, S. Wall and M. E. Rognes. In vivo estimation of elastic heterogeneity in an infarcted human heart, Biomechanics and Modeling in Mechanobiology, 2018.

[20] A. Tveito, K. H. Jæger, M. Kuchta, K.-A. Mardal and M. E. Rognes. A cell-based framework for numerical modelling of electrical conduction in cardiac tissue. Frontiers in Physics, Computational Physics, 2017.

[19] G. Pizzichelli, B. Kehlet, Ø. Evju, B. Martin, M. E. Rognes, K.-A. Mardal and E. Sinibaldi. Numerical study of intrathecal drug delivery to a permeable spinal cord: effect of catheter position and angle. Computer Methods in Biomechanics and Biomedical Engineering, 2017

[18] S. Kallhovd, M. M. Maleckar and M. E. Rognes. Inverse estimation of cardiac activation times via gradient-based optimisation. International Journal for Numerical Methods in Biomedical Engineering, 2017.

[17] M. E. Rognes, P. E. Farrell, S. W. Funke, J. E. Hake and M. M. C. Maleckar. cbcbeat: an adjoint-enabled framework for computational cardiac electrophysiology. Journal for Open Source Software, 2017.

[16] G. Balaban, H. Finsberg, H. H. Odland, M. E. Rognes, S. Ross, J. Sundnes and S. Wall. High resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. International Journal for Numerical Methods in Biomedical Engineering, 2017.

[15] G. Balaban, M. S. Alnæs, J. Sundnes and M. E. Rognes. Adjoint multi-start based estimation of cardiac hyperelastic material parameters using shear data. Biomechanics and Modeling in Mechanobiology, vol. 15(6), pp. 1509-1521, 2016.

[14] M. Alnæs, J. Blechta, J. Hake, A. Johansson, B. Kehlet, A. Logg, C. Richardson, J. Ring, M. E. Rognes and G. N. Wells. The FEniCS Project Version 1.5. Archive of Numerical Software, vol. 3(100), 2015.

[13] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A Nitsche-based cut finite element method for a fluid-structure interaction problem. Communications in Applied Mathematics and Computational Science, vol. 10(2), pp. 97-120, 2015.

[12] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A stabilized Nitsche overlapping mesh method for the Stokes problem. Numerische Mathematik, vol. 128(1), pp. 73–101, 2014.

[11] A. Massing, M. G. Larson, A. Logg and M. E. Rognes. A stabilized Nitsche fictitious domain method for the Stokes problem. Journal of Scientific Computing, vol. 61(3), pp. 604–628, 2014.

[10] M. S. Alnæs, A. Logg, K. B. Ølgaard, M. E. Rognes and G. N. Wells. Unified Form Language: A domain-specific language for weak formulations of partial differential equations. ACM Transactions on Mathematical Software, vol. 40(2), 2014.

[9] M. E. Rognes, D. A. Ham, C. J. Cotter and A. T. T. McRae. Automating the solution of PDEs on the sphere and other manifolds in FEniCS 1.2. Geoscientific Model Development, vol. 6, pp. 2099–2119, 2013.

[8] P. E. Farrell, D. A. Ham, S. W. Funke and M. E. Rognes. Automated derivation of the adjoint of high-level transient finite element programs. SIAM Journal on Scientific Computing, vol. 35(4), pp. 369–393, 2013.

[7] M. E. Rognes and A. Logg. Automated goal-oriented error control I: stationary variational problems. SIAM Journal on Scientific Computing, vol. 35(3), pp. 173–193, 2013.

[6] L. Vynnytska, M. E. Rognes and S. R. Clark. Benchmarking FEniCS for mantle convection simulations. Computers & Geosciences, vol. 50, pp. 95-105, 2013.

[5] A. Tveito, G. T. Lines, M. E. Rognes and M. M. Maleckar. An analysis of the shock strength needed to achieve defibrillation in a simplified mathematical model of cardiac tissue. International Journal of Numerical Analysis and Modeling, vol. 9(3), pp. 644–657, 2012.

[4] M. E. Rognes and R. Winther. Mixed finite element methods for linear viscoelasticity with weak symmetry. Mathematical Models and Methods in Applied Science, vol. 20(6), pp. 955–985, 2010.

[3] M. E. Rognes, M. C. Calderer and C. A. Micek. Modelling of and mixed finite element methods for gels in biomedical applications. SIAM Journal of Applied Mathematics, vol. 70(4), pp. 1305–1329, 2009.

[2] M. E. Rognes, R. C. Kirby and A. Logg. Efficient assembly of H(div) and H(curl) conforming finite elements. SIAM Journal on Scientific Computing, vol. 36(6), pp. 4130–4151, 2009.

[1] D. N. Arnold and M. E. Rognes. Stability of Lagrange elements for the mixed Laplacian. Calcolo, vol. 46(4), pp. 245–260, 2009.

#### Chapters in books

[A5] G. Halnes, K. H. Pettersen, L. Øyehaug, M. E. Rognes and G. T. Einevoll. Astrocytic ion dynamics: implications for potassium buffering and liquid flow. Accepted for publication in Computational Glioscience, Springer Series in Computational Neuroscience, edited by M. D. Pitta and H. Berry, Springer, 2019.

[A4] M. E. Rognes. Automated Testing of Saddle Point Stability Conditions. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A3] A. Logg, K. B. Ølgaard, M. E. Rognes and G. N. Wells. FFC: the FEniCS Form Compiler. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A2] R. C. Kirby, A. Logg, M. E. Rognes and A. R. Terrel. Common and Unusual Finite Elements. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.

[A1] L. Vynnytska, S. R. Clark and M. E. Rognes. Dynamic Simulations of Convection in the Earth’s Mantle. In Automated solution of differential equations by the finite element method, edited by A. Logg, K.-A. Mardal and G. N. Wells, Springer-Verlag, 2012.